Exploring differential topic models for comparative summarization of scientific papers

He, Lei, Li, Wei and Zhuge, Hai (2016). Exploring differential topic models for comparative summarization of scientific papers. IN: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics. Association for Computational Linguistics.

Abstract

This paper investigates differential topic models (dTM) for summarizing the differences among document groups. Starting from a simple probabilistic generative model, we propose dTM-SAGE that explicitly models the deviations on group-specific word distributions to indicate how words are used differentially across different document groups from a background word distribution. It is more effective to capture unique characteristics for comparing document groups. To generate dTM-based comparative summaries, we propose two sentence scoring methods for measuring the sentence discriminative capacity. Experimental results on scientific papers dataset show that our dTM-based comparative summarization methods significantly outperform the generic baselines and the state-of-the-art comparative summarization methods under ROUGE metrics.

Divisions: Engineering & Applied Sciences > Computer science
Engineering & Applied Sciences > Computer science research group
Engineering & Applied Sciences > Systems analytics research institute (SARI)
Additional Information: -This work is licenced under a Creative Commons Attribution 4.0 International License. License details: http:// creativecommons.org/licenses/by/4.0/
Event Title: 26th International Conference on Computational Linguistics
Event Type: Other
Event Dates: 2016-12-11 - 2017-02-17
Published Date: 2016-12-11
Authors: He, Lei
Li, Wei
Zhuge, Hai

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